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Cloud-mediated radiative forcing of climate due to aerosols simulated by newly developed two-way coupled WRF-CMAQ during 2006 TexAQS/GoMACCS over the Gulf.

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Presentation on theme: "Cloud-mediated radiative forcing of climate due to aerosols simulated by newly developed two-way coupled WRF-CMAQ during 2006 TexAQS/GoMACCS over the Gulf."— Presentation transcript:

1 Cloud-mediated radiative forcing of climate due to aerosols simulated by newly developed two-way coupled WRF-CMAQ during 2006 TexAQS/GoMACCS over the Gulf of Mexico and eastern U.S. Shaocai Yu, Rohit Mathur, Jonathan Pleim, David Wong, Steve Howard, and S.T. Rao Atmospheric Modeling and Analysis Division, National Exposure Research Lab, U.S. EPA, RTP, NC 27711

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5 Meteorological Model WRF modeling System:  x=12 km, 4 km 34 layers Land-Surface: PX LSM PBL: ACM2 Cloud Physics: Lin, Morrison Cumulus: Kain-Fritsch Shortwave: RRTMg, or CAM Longwave: RRTMg, or CAM Coupler Chemical Transport Model CMAQ Modeling System: Photochemistry: CB05 59 organic and inorganic species, 156 chemical reactions Aerosol module: AE6 3 lognormal modes, organic and inorganic Emission: SMOKE In-line emissions for biogenic species AQPREP Prepares virtual CMAQ compatible input met. files CMAQ-mixactivate: cloud drop number conc. Direct forcing: Aerosol size, composition, conc. Two-way coupled WRF-CMAQ modeling System (Interaction and feedback)

6 Aerosols: number, size, chemical composition CCN activation  Cloud droplet number Cloud microphysics (Lin, Morrison): cloud vapor and water, rain, ice, snow, graupel Cloud effective radius (r e ), COD The 1 st and 2 nd IAF Coupled WRF-CMAQ aerosol simulation Aerosol activation scheme ( Abdul-Razzak and Ghan, 2000, 2002 ) Updraft velocity, liquid water content (WRF) Radiative transfer model: CAM: r e (4-20)  m; RRTMg: r e ( 2-60)  m Met fields (WRF) Sulfate, BC, dust Ice number Conc., IN Cloud microphysics (Morrison, Lin): cloud vapor and water, rain, ice, snow, graupel Ice effective radius (r ie ), IOD Glaciation IAF

7 Tracks of ship Tracks of P-3 flights

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9  Model domain (12 km and 4 km), 8/1 to 8/31/2006 CONUS “5x” Domain 1.WRF-CMAQ 2.WRF-CMAQ-PM 12 km domain 4 km nested domain over Gulf of Mexico

10  Results (preliminary): Evaluation for PM 2.5 at AIRNOW sites  Both underpredicted obs PM 2.5 by -17% (Lin) and -14% (Morrison) Obs14.2±8.0 CAMlin-1211.8±7.1 CAMmor-1212.1±7.4

11  Results (preliminary): at IMPROVE sites  Both underpredicted obs PM 2.5 by -28% (Lin) and -25% (Morrison) mainly because of underestimation of SO 4 2- and OC NMB (%) meanObsLinMorrisonLinMorrison SO 4 2- 3.182.692.78-15-12 NO 3 - 0.220.210.23-42 PM 2.5 8.496.136.37-28-25 OC1.401.121.16-20-17 EC0.330.28 -17-15

12  Results: (8/4/2006, 3:00 PM EST), ground-level PM 2.5 and CCN1 (S=0.02%), CCN2 (S=0.05%), CCN3 (S=0.1%)  CAM-Lin:  CAM-Morrison:

13  Results: (8/4/2006, 12 UTC), Ground-level PM 2.5, Cloud drop #, Cloud LWC, Droplet effective radius  CAM-Lin:  CAM-Morrison: w4wrfout_d01_2006-08-04_00.ncf Effective radius (  m)

14  Results ( Shortwave cloud forcing ) Monthly means of modeled SWCF to compare with CERES obs (preliminary results)  Modeled SWCF close to Obs over GA area  Model underestimated the Obs SWCF over central and NE areas ( underestimated cloud ) 12 km (CAM-Lin) ~250 km (2.5 degree) 12 km (CAM-Mor)

15  Results (Shortwave cloud forcing) Monthly means of modeled SWCF: Interpolate to 12 km resolution for model 12 km (CAM-Lin) ~12 km (CERES) 12 km (CAM-Mor)

16  Results (Shortwave cloud forcing) Monthly means of modeled SWCF: Interpolate to 4 km resolution for model 4 km (CERES) 4 km (CAM-Lin) 4 km (CAM-Mor)

17  Results (Shortwave cloud forcing) Comparison of Monthly means SWCF:  12-km simulations underpredicted obs SWCF by more than -23%.  Mean: (watts m -2 ) Obs-12-48.5±20.7NMB (%) CAMlin-12-27.8±10.4-42 CAMmor-12-37.2±11.9-23 Obs-4-33.3±9.2 CAMlin-4-23.5±7.5-30 CAMmor-4-30.2±10.1-9-9  4-km simulations are better than 12-km, especially for Morrison scheme.

18  Results (Longwave cloud forcing) Monthly means of modeled LWCF: Interpolate to 12 km resolution for CERES 12 km (CAM-Lin) ~12 km (CERES) 12 km (CAM-Mor)

19  Results (Longwave cloud forcing) Monthly means of modeled LWCF: Interpolate to 4 km resolution for CERES 4 km (CAM-Lin) ~4 km (CERES) 4 km (CAM-Mor)

20  Results (Longwave cloud forcing) Comparison of Monthly means LWCF:  Mean: (watts m -2 ) Obs-1232.2±4.5 CAMlin-1221.7±4.9-32.6 CAMmor-1230.4±7.5-5.6 Obs-425.3±4.2 CAMlin-424.1±9.4-4.7 CAMmor-432.5±11.028.6  12-km simulations underpredicted obs LWCF by more than -5.6%.  4-km simulations are better than 12-km, especially for Lin scheme; Morrison scheme overestimated LWCF NMB (%)

21 Contacts: Brian K. Eder email: eder@hpcc.epa.gov www.arl.noaa.gov/ www.epa.gov/asmdnerl

22  Results (preliminary) : Meteorology: Radiation Models have slightly higher radiation than Obs with better results for 4 km run Missing observed cloud cover caused overestimation during the daytime  Mean (at Alabama-Coushatta, TX) Alabama-Coushatta, TX Obs270±333 CAMlin-12299±359 CAMmor-12289±350 CAMlin-4296±360 CAMmor-4289±352

23  Results ( Longwave cloud forcing ) Monthly means of modeled LWCF to compare with CERES obs (preliminary results)  Modeled LWCF  Model underestimated the Obs LWCF over central and NE areas ( underestimated cloud ) 12 km (CAM-Lin) ~250 km (2.5 degree) 12 km (CAM-Mor)

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